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Related Experiment Video

Updated: Jun 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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An efficient segmentation model for abnormal chicken droppings recognition based on improved deep dual-resolution

Pengguang He1,2,3, Rui Wu1,2,3, Da Liu1,3

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang 310058, China.

Journal of Animal Science
|April 8, 2024
PubMed
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This summary is machine-generated.

This study developed a lightweight segmentation model (DDRNet-s-KD) for automated monitoring of abnormal chicken droppings. The model achieves high accuracy and speed, making it suitable for edge devices in farms.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Chicken droppings characteristics indicate flock health.
  • Previous object detection methods for droppings faced labeling and accuracy challenges.
  • Edge computing in farms requires efficient, real-time algorithms.

Purpose of the Study:

  • Develop a lightweight segmentation model for automated abnormal chicken droppings monitoring.
  • Optimize the model for deployment on resource-constrained edge devices.
  • Improve accuracy and inference speed compared to existing methods.

Main Methods:

  • Redefined chicken droppings recognition as a segmentation task.
  • Enhanced a base segmentation network (DDRNet) with attention mechanisms and multi-loss functions.
Keywords:
Edge deviceslightweightmodel compressionsemantic segmentation

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  • Developed a lightweight version (DDRNet-s-KD) using group convolution and knowledge distillation.
  • Quantized the model to 8-bit integers and converted to TensorRT format for edge deployment.

    • Main Results:

    • The DDRNet-s-KD model achieved a mean Dice coefficient (mDice) of 79.43% and 86.10 FPS.
    • Compared to the benchmark, mDice increased by 2.91% and FPS by 61.2%.
    • The quantized model size was reduced to 13.7 MB (82.96% reduction) and achieved 137.51 FPS on Jetson Xavier NX.

    Conclusions:

    • The proposed lightweight segmentation model is effective for monitoring abnormal chicken droppings.
    • The model's efficiency and small size are suitable for edge device deployment in agricultural settings.
    • This approach offers a valuable reference for other agricultural embedded systems.